LS-SVM Based Intrusion Detection Using Kernel Space Approximation and Kernel-Target Alignment

For least squares support vector machines (LS-SVM) based intrusion detection method, there is a big obstacle that the amount of audit data for modelling is very large even for a small network scale, so it's impractical to directly train LS-SVM using original training datasets. Furthermore, LS-SVM adopts equality constraints and squared functions, which leads to solve an easy-to-compute linear system, however followed is the lack of sparseness, all training data will become the support vector of LS-SVM, which cause the low intrusion detection speed. This paper proposed a novel LS-SVM intrusion detection model using kernel space approximation through greedy searching, thus constructed a subspace basis of original space populated by training data. Through this approximation, the training data was downsized and consequently the number of support vectors of ultimate LS-SVM model were reduced, which greatly helped to improve the response time of intrusion detection. The kernel-target alignment method was utilized to obtain optimal Gaussian kernel parameter and 10-fold cross-validate method to obtain optimal trade-off parameter. The MIT's KDD Cup 99 dataset was used to evaluate our present model, and the results clearly demonstrate that the method can be an effective way for fast intrusion detection